abstract = "Since the subprime crisis, the variance of housing
price is receiving increasing attention especially
because of its complexity and practical applications.
This paper applies the flexible neural tree model for
forecasting the housing price index (HPI). The optimal
structure is developed using the modified breeder
genetic programming (MBGP) and the free parameters
encoded in the optimal tree are optimized by the
particle swarm optimization (PSO), and a new fitness
function based on error and Occam's razor is used for
for balancing of accuracy and parsimony of evolved
structures. Based on the HPI of Shandong province, the
performance and efficiency of the applied model are
evaluated and compared with the classical multilayer
feedforward network (MLFN) and support vector machine
(SVM) models.",